Dual-sensor tool optical data processing through master sensor standardization

ABSTRACT

A method may include transforming optical responses for a fluid sample to a parameter space of a downhole tool. The optical responses are obtained using a first operational sensor and a second operational sensor of the downhole tool. Fluid models are applied in the parameter space of the downhole tool to the transformed optical responses to obtain density predictions of the fluid sample. The density predictions of the first operational sensor are matched to the density predictions of the second operational sensor based on optical parameters of the fluid models to obtain matched density predictions. A difference between the matched density predictions and measurements obtained from a densitometer is calculated, and a contamination index is estimated based on the difference.

BACKGROUND

Conventional multi-sensor downhole tools include two (or more) opticalsensors, each of which is supported by its own signal standardizationand fluid characterization algorithms (also called fluid models) forreal-time optical fluid analysis. The fluid models are calibrated in asynthetic database and often require frequent updates with expansion ofthe database.

Frequent upgrades to the sensor design require frequent calibration andmaintenance of the fluid models for each sensor. Further, with anincrease in the number of downhole tools, calibrating and maintainingthe optical sensors becomes costly and time consuming. In addition,since different optical parameters are selected as fluid model inputsfor different optical sensors, additional efforts are required forinterpreting data obtained from multi-sensor tools to evaluate whetherthe predictions by the two or more optical sensors regarding the fluidin the flow line are consistent with each other, or to confirm whetherany inconsistencies in the predictions are because of using differentfluid model inputs. To simplify data interpretation, current practiceoften chooses predictions from a single sensor as the basis of analysis,requiring further improvement to maximize the underlying value of theother sensor(s).

BRIEF DESCRIPTION OF THE DRAWINGS

The following figures are included to illustrate certain aspects of thepresent disclosure and should not be viewed as exclusive embodiments.The subject matter disclosed is capable of considerable modifications,alterations, combinations, and equivalents in form and function, withoutdeparting from the scope of this disclosure.

FIG. 1 illustrates a calibration system used to calibrate an opticalsensor.

FIG. 2 illustrates a general transformation model framework including aforward transformation and a reverse transformation between a toolparameter space and a synthetic parameter space with neural networks.

FIG. 3 depicts a hierarchical structure for reverse transformationmodels.

FIGS. 4A-4C illustrate a cross-sensor standardization modeling of sensordata in tool parameter space between an operational sensor (OS) data anda master sensor (MS) data for a field set from fluids measured by adownhole tool.

FIGS. 5A-5C illustrate a transformation from standardized master sensordata in tool parameter space of the master sensor to a syntheticparameter space (SMS) of the master sensor.

FIG. 6 is a flowchart of a method for dual sensor optical tool dataprocessing through master sensor standardization.

FIG. 7 is a flowchart of a method for estimating a contamination indexof a live oil sample.

FIGS. 8A-8D compare results of post-processed fluid predictionsregarding methane concentration, live density, gas/oil ratio, and resinconcentration, respectively, of a medium oil sample using conventionalindividual-sensor based fluid models and the master-sensor based fluidmodels.

FIGS. 9A-9D compare post-processed master sensor predictions of livedensity, methane concentration, saturate concentration and aromaticsconcentration, respectively, of a volatile oil sample obtained from afield job, using transformed optical inputs from the upper and loweroperational sensors of a downhole tool.

FIG. 10 is a drilling system configured to use a dual-sensor downholetool including upper and lower operational sensors, and employing one ormore principles of the present disclosure for modifying a drillingparameter or configuration in a measurement-while-drilling (MWD) and alogging-while-drilling (LWD) operation.

FIG. 11 is a wireline system configured to use a dual-sensor downholetool including upper and lower operational sensors and employing one ormore principles of the present disclosure during formation testing andsampling.

FIG. 12 illustrates an exemplary processing system for configuringand/or controlling the calibration system of FIG. 1 and the downholetools of FIGS. 9 and 11.

DETAILED DESCRIPTION

Embodiments described herein relate to a method of processing andinterpreting data obtained by a downhole tool having two optical sensors(also referred to as a dual-sensor optical tool) through master sensorstandardization for downhole optical fluid analysis. The exemplarymethod may efficiently evaluate the quality of optical datatransformation and the uncertainty of fluid model prediction, which areassociated with fluid phase and contamination, by comparing dual-sensorpredictions that are standardized to a single master sensor framework.

According to embodiments disclosed herein, the real-time measurements ofeach of the two optical sensors from any dual-sensor tool can be mappedinto a single master sensor tool parameter space using a non-linearcross-sensor standardization algorithm. Then, a different cross-spacedata transformation algorithm can be applied to the master sensor thatfurther converts previously mapped optical signals for each opticalsensor from the tool parameter space to a synthetic parameter space foruse with pre-calibrated fluid models. Thereafter, the candidate fluidmodels of the master sensor can be used to predict the fluidcompositions and other properties from the transformed optical inputs ofeach operational sensor. Since the predictions from the dual-sensoroptical tool data are compared over the fluid models with same inputs,the uncertainty of using sensor-dependent inputs may be reduced, and theresults from the dual-sensor data processing may be used in conjunctionwith other measurements of testing and sampling system, such asdensitometer, bubble point, fluid capacitance, and pumpout rate, toprovide integrated solutions on quality data transformation andprediction associated with fluid phase and contamination.

The exemplary method may be used to reduce the cost of sensorcalibration and data management for fluid characterization and reducethe uncertainty of dual-sensor optical data interpretation.

FIG. 1 illustrates an exemplary calibration system 100 that may be usedto calibrate one or more optical elements used in an optical sensor. Asillustrated, system 100 may include a measurement system 102 in opticalcommunication with one or more optical sensors 104 (shown as 104 a, 104b, 104 c . . . 104 n) that are to be calibrated. Each optical sensor 104a-n may include, without limitation, an optical band-pass filter or amultivariate optical element/integrated computational element (e.g., anICE core). The measurement system 102 may circulate one or morereference fluids with different chemical compositions and properties(i.e., methane concentration, aromatics concentration, saturatesconcentration, Gas-Oil-Ratio—GOR—, and the like) through an optic cell106 over widely varying calibration conditions of temperature, pressure,and density, such that optical transmission and/or reflectionmeasurements of each reference fluid in conjunction with each opticalelement 104 a-n may be made at such conditions.

The measurement system 102 may include an opticalpressure-volume-temperature (PVT) instrument, and the reference fluidscirculated in the measurement system 102 may include representativefluids commonly encountered in downhole applications. The system 100 maycollect output signals from each optical element 104 a-n for eachspecified reference fluid at varying calibration conditions. In somecases, the reference fluids may include representative fluids that areeasy to operate for manufacturing calibration, such as dodecane,nitrogen, water, toluene, 1-5 pentanediol, and two liquid crude oils orfluids with no gas concentration (e.g., dead oil). The crude reservoiroils used as reference fluids may be, for example, global oil library 13(or “GOL13”), and global oil library 33 (or “GOL33”). In other cases,the reference fluids may include samples of live oils mixed with deadoil and hydrocarbon gas, such as methane for example, and the samples ofhydrocarbon gases and/or CO₂. Manufacturing calibration of the opticalsensor may serve the need of detector output re-scaling or instrumentstandardization.

The measurement system 102 may vary each reference fluid over severalset points spanning varying calibration conditions. To accomplish this,as illustrated, measurement system 102 may include a liquid chargingsystem 108, a gas charging system 110, a temperature control system 112,and a pressure control system 114. The liquid charging system 108injects reference fluids into the fluid circuit to introduce fluidvarying perturbations such that calibrating the optical elements 104 a-nwill incorporate all the expected compounds found in the particularreference fluid. The gas charging system 110 may inject known gases(e.g., N₂, CO₂, H₂S, methane, propane, ethane, butane, combinationsthereof, and the like) into the circulating reference fluids. Thetemperature control system 112 may vary the temperature of the referencefluid to simulate several temperature set points that the opticalelements 104 a-n may encounter downhole. Lastly, the pressure controlsystem 114 may vary the pressure of the reference fluid to simulateseveral pressure set points that the optical elements 104 a-n mayencounter downhole.

The optic cell 106 is fluidly coupled to each system 108, 110, 112, and114 to allow the reference fluids to flow therethrough and recirculateback to each of the systems 108, 110, 112, and 114 in a continuous,closed-loop fluid circuit. While the reference fluid circulates throughoptic cell 106, a light source 116 emits electromagnetic radiation 118that passes through optic cell 106 and the reference fluid flowingtherethrough. As the electromagnetic radiation 118 passes through theoptic cell 106 it optically interacts with the reference fluid andgenerates sample interacted light 120, which includes spectral data forthe particular reference fluid circulating through the measurementsystem 102 at the given calibration conditions or set points. The sampleinteracted light 120 may be directed toward optical sensors 104 a-nwhich, as illustrated, may be arranged or otherwise disposed on a sensorwheel 122 configured to rotate in the direction A. While shown asarranged in a single ring on the sensor wheel 122, optical sensors 104a-n may alternatively be arranged in two or more rings on the sensorwheel 122. According to embodiments disclosed herein, a typical downholetool may include two sensor wheels 122 that may be separated by apredetermined distance (e.g., 1 meter) from each other. One of the twosensor wheels 122 may be referred to as an upper sensor wheel 122 and islocated uphole from the other sensor wheel 122 which may be referred toas a lower sensor wheel 122. In the present disclosure, a sensor wheel122 including optical sensors 104 a-n may also be referred to as anoperational sensor (OS) 122. Thus, the downhole tool may include anupper operational sensor 122 and a lower operational sensor 122.

During calibration, sensor wheel 122 may be rotated at a predeterminedfrequency such that each optical sensor 104 a-n may optically interactwith the sample interacted light 120 for a brief period and sequentiallyproduce optically interacted light 124 that is conveyed to a detector126. Detector 126 may be generally characterized as an opticaltransducer and may comprise, but is not limited to, a thermal detector(e.g., a thermopile), a photo-acoustic detector, a semiconductordetector, a piezo-electric detector, a charge coupled device (CCD)detector, a video or array detector, a split detector, a photon detector(e.g., a photomultiplier tube), photodiodes, and any combinationthereof. Upon receiving individually detected beams of opticallyinteracted light 124 from each optical sensor 104 a-n, detector 126 maygenerate or otherwise convey corresponding response signals 128 to adata acquisition system 130. A data acquisition system 130 may timemultiplex each response signal 128 received from the detector 126corresponding to each optical sensor 104 a-n. A corresponding set ofresulting output signals 132 is subsequently generated and conveyed to adata analysis system 134 for processing and providing input parametersfor various fluid predictive models with use of outputs from eachoptical element 104 a-n as a candidate variable.

Once sensor wheel 122 is calibrated, one or more calibrated sensorwheels 122 (or operational sensors 122) may then be installed on adownhole tool with other system components, for assembly validationtesting. To validate the optical response of the sensor assembly, thesensor may be placed in an oven that regulates the ambient temperatureand pressure. The reference fluids used to calibrate sensor wheel 122may then be selectively circulated through the optical sensor at similarset points used to calibrate the optical sensors 104 a-n. Moreparticularly, the reference fluids may be circulated through the opticalsensor at various set point downhole conditions (i.e., elevatedpressures and temperatures) to obtain measured optical responses.

The optical sensors 104 a-n are calibrated using the response of thesensors to reference fluids in a tool parameter space. On the otherhand, fluid spectroscopic analysis and fluid predictive modelcalibration using a large amount of data in a standard oil library isperformed in a synthetic parameter space (also called Optical-PVT dataspace). Synthetic sensor responses for each sensor in the downhole toolare calculated as a dot product of full-wavelength-range of fluidspectrometry and sensor element spectrum excited by a light source. Thevalue of the dot product may vary nonlinearly or linearly compared tothe actual sensor response due to the difference between themathematical approximation used in calculating synthetic sensor responseand the real system implementation. To compensate for the differenceabove, the measurement data from the sensors in the downhole tool can betransformed from the tool parameter space to the synthetic parameterspace through a reverse transformation algorithm before applying fluidpredictive models. In some embodiments, fluid predictive models arecalibrated with different synthetic optical inputs, and saved in anoptical fluid model base. This provides sufficient adaptability indealing with data transformation uncertainty and improves the formationfluid compositional analysis and field data interpretation.

In current practice, an optical fluid model is sensor dependent for eachdownhole tool used for measurement. An optical fluid model includes datatransformation (i.e., standardization) models and property predictivemodels. To provide adequate flexibility for optical data processing andinterpretation, an optical fluid model includes the following candidateconstituents: transformation models calibrated on selected referencefluids through reverse transformation, transformation models calibratedon selected reference fluids through forward transformation, andpredictive models calibrated on both Optical-PVT database and sensorwheel 122 data spaces. Depending on the data space in which the fluidproperty predictive models are calibrated, data transformation modelsconvert measured or simulated optical sensor output between a toolparameter space and a synthetic parameter space. FIG. 2 illustrates onesuch transformation.

FIG. 2 illustrates an embodiment of a general transformation modelframework including a forward transformation 205 and a reversetransformation 203 between data in a tool parameter space 201 and asynthetic parameter space 202 with a non-linear algorithm. In someembodiments, the non-linear algorithm used to implement reversetransformation 203 is a neural network model. In some embodiments theforward 205 and reverse transformation 203 includes a multi-input,multi-output neural network that may be applied by data analysis system134 of FIG. 1 to optical responses. The model that converts the actualoptical sensor response sensors (SW/Ch01-Ch0n) from tool parameter space201 to synthetic parameter space 202 (PVT/Ch01-Ch0n) is reversetransformation 203. The model that converts data from syntheticparameter space 202 into tool parameter space 201 is forwardtransformation 205. Although the illustrated general transformationmodel framework in FIG. 2 is configured with multi-input/multi-outputnon-linear neural networks, there is no limitation in using othernon-linear and linear transformation algorithms withsingle-input/single-output and multi-input/single-output configurations.

FIG. 3 illustrates an embodiment of a hierarchical structure for reversetransformation models 302. The variations of transformation models 302may include converting optical sensors 304 for each optical sensor in asingle model, converting the disjoined optical sensors in severaldetector-based models 306, or converting only selected sensors 308 ofinterest each time in different individual models. Compared to a singlemodel implementation, multi-model options can improve the reliability ofdata construction in the output (i.e., transformed) parameter domain(e.g., synthetic parameter space 202, cf. FIG. 2) if one or more of theoptical sensors (e.g., tool parameter space 201, cf. FIG. 2), as atransformation input, experience a problem. A plurality of referencefluid blocks 310-320, at the bottom of the hierarchical structure andcoupled to the various sensors 304-308, represent the transformationmodels that can be built based on different reference fluids (e.g.,minimum number of reference fluids 310, 314, 318 and extended referencefluids 312, 316, 320). The minimum number of reference fluids may referto the seven representative fluids discussed above. These referencefluids are safe to use in a laboratory configuration and easy to cleanfor testing purposes. Optical sensor responses (e.g., tool parameterspace 201) generally have a wide dynamic range as a representation ofdiverse fluids in an existing Optical-PVT database. Extended referencefluids often include one or more fluids such as live oil, natural gas,and/or gas condensates to cover a wider dynamic range and provide a morerobust transformation model.

In some embodiments, reverse transformation 203 (FIG. 2) converts sensormeasurements from tool parameter space 201 into synthetic parameterspace 202 prior to applying fluid characterization models. Accordingly,fluid characterization models use data from synthetic parameter space202 as input to obtain information such as fluid composition, andphysical properties of the fluid. Forward transformation 205 (FIG. 2)can be used to convert a whole set of simulated optical sensor responsesfrom synthetic parameter space 202 to tool parameter space 201 prior todeveloping predictive models on tool parameter space 201. As seen inFIG. 2, forward transformation 205 can be created by switching the inputand the output of a neural network model. In other words, using asynthetic-sensor response as an input and a measured sensor wheel sensorresponse as an output, a neural network can then be trained to calibrateforward transformation algorithms.

As will be appreciated, a hierarchical structure for the reversetransformation models 302, as illustrated in FIG. 3, can also be appliedto forward transformation models. After forward transformation 205 isdeveloped, it can be used to convert the synthetic sensor responses ofthe global samples in synthetic parameter space 202 into tool parameterspace 201. Then the fluid property predictive models can be calibratedin tool parameter space 201, and the further transformation is notneeded in field data processing because measured optical responses fromthe sensor can be used as model inputs directly for fluid compositionalanalysis. Compared to the reverse transformation, which applies on-linesensor data conversion each time before making a fluid prediction,forward transformation usually only applies one time off-line to convertsynthetic sensor responses for fluid prediction model development.However, reverse and forward transformations have different complexitywith neural network implementation. Compared to reverse transformation,forward transformation may require a larger number of reference fluidsfor calibration, and consequently may induce higher uncertainty in fluidmodel development with use of transformed synthetic database. Therefore,reverse transformation is selected hereafter as general framework forcross-space transformation and used in conjunction with cross-sensortransformation described below for dual-sensor tool optical dataprocessing.

FIGS. 4A-4C illustrate a cross-sensor transformation of sensor data intool parameter space from an operational sensor (OS) data 400 a to amaster sensor (MS) data 400 b for a field set 450 from fluids measuredby the dual-sensor downhole tool. The operational sensor (OS) data 400 amay be tool parameter space data from either the upper operationalsensor 122 or the lower operational sensor 122 installed on thedual-sensor downhole tool deployed in a field application, whereas themaster sensor (MS) data 400 b may be tool parameter space data from amaster sensor. Without loss of generality, a total of twenty-four sensorelements or channels 401 are used to collect operational sensor (OS)data 400 a and twenty-four sensor elements or channels 411 are used inthe master sensor (MS) data 400 b. In some embodiments, the twenty-foursensor elements in each of the upper and lower operational sensors, andthe master sensor may include ICE elements and narrow band-pass (NBP)filters, among other optical elements.

In an embodiment, the optical sensor configuration and ICE design of thetwenty-four sensor elements in operational sensor data 400 a may bedifferent from that of the twenty four sensor elements in master sensordata 400 b. For example, data in channel 1 of operational sensor data400 a may be associated with a methane ICE fabricated according to afirst design, and a corresponding data in channel 1 of master sensordata 400 b may be associated with a methane ICE fabricated according toa second design. Accordingly, the first design may include a firstnumber of alternating dielectric layers, each of the layers having aspecific thickness determined according to the first design, and thesecond design may include a second number of alternating dielectriclayers, each of the layers having a specific thickness determinedaccording to the second design. In some embodiments, sensor elements 401may include at least one NBP in the ultra-violet (UV)-visible wavelengthdomain (approximately from 400 nm to 750 nm), whereas sensor elements411 may include at least one NBP in the near-infrared (NIR) wavelengthdomain (approximately from 750 nm to 2500 nm). In another embodiment,the upper and lower operational sensors 122 may each have a differentconfiguration and design. For instance, the upper operational sensor mayinclude ICE elements while the lower operational sensor 122 may includeNBP filters.

FIG. 4A depicts operational sensor data 400 a that shows opticalresponses obtained by the twenty-four optical sensor elements (orchannels) 401 of either the upper or lower operational sensor 122.Accordingly, the data illustrated in FIG. 4A may be obtained from fluidsmeasured in the wellbore by the dual-sensor downhole tool including theupper and lower operational sensors. The abscissae in FIG. 4A includeintegers indicative of each of the twenty four optical sensor elements(or channels) 401 in one of the upper or lower operational sensor, andthe ordinate indicates a value (intensity) for the signal produced byeach optical sensor element 401. The value for the signal of eachoptical sensor element 401 may include a normalized voltage proportionalto an intensity of an interacted light received in a detector from eachoptical sensor element 401. Accordingly, each trace having twenty-fourdata points in FIG. 4A corresponds to a wellbore fluid measured by anyone of the upper or lower operational sensor 122 of the downhole toolusing respective optical sensor elements 401.

FIG. 4B depicts master sensor data 400 b obtained by cross-sensortransformation 413 of the operational sensor data 400 a using amulti-input, multi-output neural network transformation algorithm 415.The neural network transformation algorithm 415 is pre-calibrated on theselected reference fluids which includes, but are not limited to,petroleum representative samples of dead oil, live oil, natural gasand/or gas condensates, water, and nitrogen for generalized training,and other featured fluids such as toluene, pentanedioal, and dodecane toensure adequate parameter range of optical response for each sensorelement. The optical sensor responses used as calibration inputs andoutputs for calibrating cross-sensor standardization algorithm areobtained from lab testing and/or simulation analysis at matchedtemperatures and pressures. The calibration data may be pre-processedwith baseline correction, normalization, and may include othercalibration inputs, such as temperature and pressure. Neural networktransformation algorithm can also be optimized for quality data mappingthrough training by adjusting the number of neurons or nodes on hiddenlayer (as shown in FIG. 4C), applying regularized training algorithm,and using ensemble predictor with transformation outputs averaged overmore than one member networks. The abscissae in FIG. 4B includesintegers indicative of each of the twenty-four optical sensor elements411 in the master sensor, and the ordinate indicates a value for thesignal produced by each optical element 411. It should be noted that areal, physical master sensor is used during the calibration, andcalibration inputs from operational sensors and outputs from mastersensor are generally pre-processed with same procedure or softwareroutine. The value for the signal of each optical element 411 mayinclude a normalized voltage proportional to an intensity of aninteracted light received in a detector from each optical element 411.Accordingly, each trace having twenty-four data points in FIG. 4Bcorresponds to the reference fluids as measured by the master sensorduring calibration. In downhole application, however, operationalsensors are the only sensors installed on the downhole optical tool, andthe master sensor could be a ‘virtual’ sensor used for data processing.In this case, each trace corresponds to the wellbore fluid as it wouldhave been measured by the master sensor had the master sensor been usedin the downhole tool instead of the upper and lower operational sensors122.

FIG. 4C shows a cross-sensor transformation 413 of operational sensordata 400 a from optical sensor 401 in one of the upper and lower sensors122, to master sensor data 400 b. As will be understood, thecross-sensor transformation 413 standardizes the operational sensor data400 a. In some embodiments, cross-sensor transformation 413 applied intool parameter space is a non-linear mapping such as a multi-input,multi-output neural network (NN) algorithm. The NN algorithm istypically implemented with an input layer, a hidden layer 415, and anoutput layer. The input layer receives transformation inputs fromoperational sensors 401. The hidden layer has a number of hidden neuronsor nodes as adjustable computing elements. Each element is equipped witha nonlinear hyperbolic tangent sigmoid or logarithmic sigmoid transferfunction to process the weighted combinational data from the input layeraccording to the specified transfer function. The output layer isassigned with same number of optical sensor elements 411 as the mastersensor. The output of each element 411 on the output layer is a weightedlinear combination of hidden neuron outputs on the hidden layer.

FIGS. 5A-5C illustrate a transformation 503 from standardized mastersensor data 400 b in tool parameter space of the master sensor to asynthetic parameter space (SMS) of the master sensor. As illustrated,twenty-four sensor elements or channels 411 are used in the mastersensor (MS) data 400 b as inputs and produce twenty four sensor elementsor channels (SMS) 501 of the master sensor in the synthetic parameterspace of the master sensor. The transformation 503 uses a cross-space orreverse transformation algorithm of the master sensor. The master sensorcross-space transformation algorithm is pre-calibrated on the samereference fluids with neural networks to convert the master sensorresponses from the tool parameter space to the synthetic parameterspace. As calibration outputs, the synthetic optical responses of themaster sensor are calculated as a dot product of spectroscopy data ofreference fluids and spectra of optical sensor elements over the samewavelength range and measured at elevated and specified temperatures andpressures, followed by baseline correction and neutral densitynormalization. As mentioned above, the abscissae in FIG. 5A includesintegers indicative of each of the twenty-four optical sensor elements411 in the master sensor, and the ordinate indicates a value for thesignal produced by each optical element 411. The abscissae in FIG. 5Bincludes integers indicative of each of the twenty four optical sensorelements in the master sensor, and the ordinate indicates a value forthe synthetic signal expected from each optical element 411 compatibleto the inputs of a fluid model.

FIG. 5C includes transformation 503 performing a cross-spacetransformation algorithm, which can be a nonlinear transformationalgorithm such as a neural network with same architecture as specifiedin FIG. 4C. In embodiments consistent with the present disclosure,transformation 503 is applied at a second concatenation stage of neuralnetwork processing that maps data from the tool parameter space of themaster sensor to the synthetic parameter space of the master sensor.Accordingly, concatenating transformation 413 with transformation 503may enable a robust workflow for downhole optical tool data processingfrom operational sensor to master sensor.

In some embodiments, transformation 413 can also be used as a non-linearfilter to generate smooth optical inputs for transformation 503. Becausetransformation 413 is equipped with hyperbolic tangent sigmoid orlogarithmic sigmoid transfer function, the output of each hidden nodecan be confined to a reasonable range in de-spiking signals ontransformation output even the optical sensor inputs are substantiallyout of the calibration range, especially for mud-filtrate corrupteddata. This feature of embodiments consistent with the present disclosureenhances data processing for fluid model validation analysis.

FIG. 6 is a flowchart of a method 600 for dual sensor optical tool dataprocessing through master sensor standardization. It should be notedthat methods consistent with the present disclosure may include at leastsome, but not all of the steps illustrated in method 600, performed in adifferent sequence. Furthermore, methods consistent with the presentdisclosure may include at least two or more steps as in method 600performed overlapping in time, or almost simultaneously.

The method 600 may include collecting measurement data using a firstoperational sensor and a second operational sensor of a downhole tool,as at 602. As mentioned above, the downhole tool may include two or moreoperational sensors 122 installed on the upper and lower positions (cf.FIG. 1). In some embodiments, the two operational sensors 122 on eachdownhole tool may have same number of optical elements, such as ICEs andNBP filters, with same designs. In other embodiments, the twooperational sensors 122 may have different number of optical elementswith different designs.

The method 600 may then standardize optical responses of eachoperational sensor to a master sensor in a tool parameter space toobtain a standardized master sensor response, as at 604. In someembodiments, the operational sensors and the master sensor may have sameconfiguration and element design. In other embodiments, the operationalsensors and the master sensor may have different configurations andelement designs. The cross-sensor standardization is performed in toolparameter space to convert operational sensor responses to master sensorresponses by using multi-input, multi-output neural networktransformation algorithm. The neural network standardization algorithmis pre-calibrated on the selected reference fluids which includes, butare not limited to, petroleum representative samples of dead oil (e.g.,oil at sufficiently low pressure that it contains no dissolved gas or arelatively thick oil or residue that has lost its volatile components),live oil (e.g., oil containing dissolved gas in solution that may bereleased from solution at surface conditions), natural gas, water, andnitrogen for generalized training; and other featured fluids such astoluene, pentanedioal, and dodecane to ensure adequate parameter rangeof optical response for each sensor element. The optical responses ofthe operational and master sensor pairs for calibrating standardizationalgorithm are obtained from lab testing and/or simulation analysis atmatched temperatures and pressures. The calibration data can bepre-processed with baseline correction, normalization and otherenvironmental correction, and neural network standardization algorithmcan be optimized to adequate complexity for quality data mapping.

Further, the method 600 may include transforming the standardized mastersensor response to a synthetic parameter space response of the mastersensor, as at 606. The master sensor cross-space transformationalgorithm is pre-calibrated on the same reference fluids with neuralnetworks to covert the master sensor responses from the tool parameterspace to the synthetic parameter space. The synthetic optical responsesof the master sensor may be calculated as a dot product of spectroscopydata of reference fluids and spectra of sensor elements over the samewavelength range and measured at elevated and specified temperatures andpressures, followed by baseline correction and neutral densitynormalization prior to calibration.

The method 600 may then apply a fluid model with the synthetic parameterspace response of the master sensor to predict a fluid characteristic,as at 608. Applying a fluid model may include testing one or moreanalyte-specific candidate models with different inputs transformed fromeach operational sensor respectively for each analyte prediction. Thefluid model are pre-calibrated with a plurality of nonlinearmulti-input, single-output neural networks or linear partial leastsquare (PLS) algorithms in synthetic parameter space on a large numberof fluid samples from a standard oil library, using synthetic mastersensor responses as candidate calibration inputs, and measured fluidcompositions and properties as calibration outputs. In an embodiment,the method may include applying a plurality of candidate fluid modelswith different candidate inputs for each fluid analyte or propertyprediction. The inputs to the candidate models can be determined from anautomatic selection algorithm such as backward stepwise input selectionor forward stepwise input selection.

The method 600 may then compare a first prediction obtained with thefluid model from the first operational sensor with a second predictionobtained with the fluid model from the second operational sensor, as at610. Evaluating the predictions may include determining the matchedpredictions with same inputs for single phase fluid, determining thevariation of predictions for multi-phase fluid, and estimating fluidcontamination with presence of oil-based mud filtrate. The fluid phasein a flow line may be determined by comparing the difference in opticalresponses recorded by the first and second operational sensors, andexamining other measurements of testing and sampling system, such asdensitometer, bubble point, fluid capacitance, and pumpout rate.Optimizing the data interpretation may use real-time processing orpost-processing routines and include synchronizing the predictions,adjusting fluid model selection, and providing self-consistentestimation based on predictions from either one of the operationalsensors or from both the first and second operational sensors. Further,the method 600 may determine a fluid characteristic from the firstprediction and the second prediction, as at 612, and optimize a welltesting and sampling operation according to the fluid characteristic, asat 614.

FIG. 7 is a flowchart of a method 700 for estimating a contaminationindex of a live oil sample. It should be noted that methods consistentwith the present disclosure may include at least some, but not all ofthe steps illustrated in method 700, performed in a different sequence.Furthermore, methods consistent with the present disclosure may includeat least two or more steps as in method 700 performed overlapping intime, or almost simultaneously.

The method 700 may determine matched density predictions of a fluidsample using master sensor fluid models, as at 702. Each of a firstoperational sensor and a second operational sensor may provide arespective live density prediction. The method 700 may then calculate adifference between density predictions and measurements obtained from adensitometer, as at 704, and estimate a contamination index based on thedifference, as at 706. Herein, the live density prediction is a densityprediction of the fluid sample including one or more dissolved gases.

FIGS. 8A-8D compare results of post-processed fluid predictionsregarding methane concentration, live density, gas/oil ratio and resinconcentration, respectively, of a medium oil sample using conventionalindividual-sensor based fluid models and the master-sensor based fluidmodels with neural networks according to embodiments disclosed. Thehorizontal coordinate represents the sample index 802 of a measurementsequence from a field job. The vertical coordinate represents the fluidproperty measured in appropriate measurement units. FIG. 8A compares theresults of a methane concentration prediction as carried out usingconventional individual-sensor based fluid models and the master-sensorbased fluid models with neural networks according to embodimentsdisclosed. The curve 822 indicates the prediction provided usingconventional fluid models from an upper operational sensor of a downholetool and the curve 824 represents the prediction provided using themaster sensor models through cross-sensor standardization. As astatistical comparison, prediction in each case is an arithmetic averageover multiple candidate fluid models with different inputs calibrated insynthetic parameter space of optical sensor on a standard oil librarydatabase. FIG. 8B compares the results on live density predictionbetween the upper operational sensor and the master sensor. FIG. 8Ccompares the results on gas/oil ratio prediction between a loweroperational sensor of the downhole tool and the master sensor. In FIG.8C, the curve 826 represents the prediction provided using fluid modelsfrom the lower operational sensor of the same downhole tool used inFIGS. 8A-8B, and the curve 824 represents the prediction provided fromthe master sensor models by using standardized data inputs from thelower operational sensor. FIG. 8D compares results on resins predictionbetween the lower operational sensor and the master sensor.

It may be seen from FIGS. 8A-8D that predictions by applyingconventional individual sensor-based fluid models and master sensorbased fluid models have good agreement with each other. Because opticaltool data processing through master sensor standardization may onlyrequire a single set of fluid models (namely, the set corresponding tothe master sensor calibration) regardless of types of operationalsensors used in the downhole tool, cost savings in fluid modelcalibration can be achieved using the exemplary method. Further, becauseof a reduction in the number of fluid models used, updating andmaintaining the optical sensor model base would become more convenientand efficient. It should be noted from FIGS. 8A-8D that the initialoptical sensor responses (e.g., sampling index less than 500) are out ofcalibration range of the fluid models due to contamination of oil-basedmud filtrates, and may thus be ignored in common practice. However, asthe operation continues, the sampling gradually approaches tostabilization, and predictions from optical responses are in closeagreement with each other, indicating that operational sensor basedfluid model calibration can be replaced by master sensor basedcalibration with equivalent quality in estimating fluid properties andreduced cost.

FIGS. 9A-9D compare post-processed master sensor predictions of livedensity, methane concentration, saturate concentration and aromaticsconcentration of a volatile oil sample obtained from a field job, usingtransformed optical inputs from the upper operational sensor and loweroperational sensor of a downhole tool. The predictions from bothoperational sensors are synchronized to a time series to better comparethe predictions obtained with the two sensors.

FIG. 9A illustrates sample graphs showing results of live densitypredictions using the exemplary method and associated application inreal-time fluid contamination analysis. It should be noted that theillustrated live density predictions are with respect to live density offormation fluids, such as love oil density, live gas density, and livecondensate density. Live density is typically estimated/predicted fromoptical inputs. It is also used to distinguish the reference fluiddensity measured with a densitometer sensor as described below. Fluidcontamination analysis is crucial during well testing and samplingoperation in collecting clean or near-clean oil samples with minimizedmud-filtrate contamination. As a cost-effective approach, fluid densityis often used to determine contamination index for real-time dataanalysis. In FIG. 9A, the live density predictions from each of lowerand upper operational sensors (curves 922 and 924, respectively) of adownhole tool using the same optical inputs have been compared againstthe densitometer measurements (curve 926) taken from the same field job.Densitometer is an additional tube-vibration based sensor for measuringflow line fluid density and is often used in conjunction with opticalsensor measurements to characterize downhole fluid properties. However,since the densitometer sensor has limitations in distinguishing thedensity of oil-based mud (OBM) and the density of oil, its applicationin fluid contamination analysis is not straightforward, and usuallyrequires complex modeling and longer sampling time. In contrast, thelive density predicted from each of the upper and the lower operationalsensors is more sensitive to the variation of OBM related fluidcontamination. As a result, in an embodiment, the difference betweendensitometer measurement and live density prediction may be used as acontamination index for OBM related field test. It may be observed fromFIG. 9A that, during early stages of analysis in which significant OBMfiltrate is present, a large difference may be observed between themeasurements from the operational sensors and the densitometermeasurements (cf. abscissae in FIG. 9A for t<18000 secs.). Thedifference, however, becomes smaller and eventually reduces to near zeroduring later stages of analysis, for example, at about 29000 seconds, atwhich relatively contamination-free fluid samples may be obtained. Usingthe master sensor fluid models for live density prediction, it ispossible to process existing field data with different optical tools,and incorporate live density prediction with available densitometermeasurements and other information and/or lab results throughdata-driven modeling for general contamination analysis.

FIG. 9B illustrates a sample graph showing results of matched predictionon methane concentration. The difference between the methaneconcentration as predicted by the upper and lower operational sensorscan be used to estimate the uncertainty of cross-sensor standardization,which may be induced by calibration error of the standardizationalgorithm and/or optical sensor signal variation after calibration. Theinconsistency of master sensor predictions using transformed opticalresponses of upper and lower operational sensors may also indicatemultiphase status in flowing line when sensing elements in differentsensors record signal variations in synchronized time constants due todynamic system responses.

FIGS. 9C and 9D illustrate sample graphs showing results of matchedprediction on saturates concentration and aromatics concentration,respectively. In this example, the predictions with master sensorresponses from either upper or lower operational sensor of the downholetool can be used for decision making because of their close similarity.The final predictions can also be obtained by averaging the outputs ofthe master sensor predictions from the upper and lower operationalsensor inputs. In reference to FIGS. 9B-9D, it may be noted that theconcentration of methane, saturates, and aromatics constitute theprimary compositions of the volatile oil sample, which is consistentwith live density prediction and densitometer measurements illustratedin FIG. 9A.

FIG. 10 is a drilling system 1000 configured to use a dual-sensordownhole optical tool including upper and lower operational sensors 122for modifying a drilling parameter or configuration in ameasurement-while-drilling (MWD) and a logging-while-drilling (LWD)operation according to the estimated borehole or formation fluidproperties. Boreholes may be created by drilling into the earth 1002using the drilling system 1000. The drilling system 1000 may beconfigured to drive a bottom hole assembly (BHA) 1004 positioned orotherwise arranged at the bottom of a drill string 1006 extended intothe earth 1002 from a derrick 1008 arranged at the surface 1010. Thederrick 1008 includes a kelly 1012 and a traveling block 1013 used tolower and raise the kelly 1012 and the drill string 1006.

The BHA 1004 may include a drill bit 1014 operatively coupled to a toolstring 1016 which may be moved axially within a drilled wellbore 1018 asattached to the drill string 1006. During operation, the drill bit 1014penetrates the earth 1002 and thereby creates the wellbore 1018. The BHA1004 provides directional control of the drill bit 1014 as it advancesinto the earth 1002. The tool string 1016 can be semi-permanentlymounted with various measurement tools (not shown) such as, but notlimited to, measurement-while-drilling (MWD) and logging-while-drilling(LWD) tools, that may be configured to take downhole measurements ofdrilling conditions. In other embodiments, the measurement tools may beself-contained within the tool string 1016, as shown in FIG. 10.

Fluid or “mud” from a mud tank 1020 may be pumped downhole using a mudpump 1022 powered by an adjacent power source, such as a prime mover ormotor 1024. The mud may be pumped from the mud tank 1020, through astandpipe 1026, which feeds the mud into the drill string 1006 andconveys the same to the drill bit 1014. The mud exits one or morenozzles arranged in the drill bit 1014 and in the process cools thedrill bit 1014. After exiting the drill bit 1014, the mud circulatesback to the surface 1010 via the annulus defined between the wellbore1018 and the drill string 1006, and, in the process, returns drillcuttings and debris to the surface. The cuttings and mud mixture arepassed through a flow line 1028 and are processed such that a cleanedmud is returned down hole through the standpipe 1026 once again.

The BHA 1004 may further include a dual-sensor downhole optical tool1030 similar to the downhole tools described above. More particularly,the dual-sensor downhole tool 1030 may have the upper and loweroperational sensors 122 (not illustrated) arranged therein, and thedownhole tool 1030 may be calibrated prior to being introduced into thewellbore 1018 using the sensor validation testing generally describedherein. Based on the real-time fluid predictions of the optical tool,one or more drilling parameters such as drilling direction orpenetration rate may be modified.

FIG. 11 illustrates a wireline system 1100 that may employ one or moreprinciples of the present disclosure. In some embodiments, wirelinesystem 1100 may be configured to use a dual-sensor downhole optical tooldescribed herein for formation testing and sampling. After drilling ofwellbore 1018 is complete, it may be desirable to know more details oftypes of formation fluids and the associated characteristics throughsampling with use of wireline formation tester. The system 1100 mayinclude a downhole tool 1102 that forms part of a wireline loggingoperation that can include the exemplary upper and lower operationalsensors, shown generally at 1104. The system 1100 may include thederrick 1008 that supports the traveling block 1013. The downhole tool1102, such as a wireline logging tool, may be lowered by wireline orlogging cable 1106 into the wellbore 1018. The downhole optical tool1102 may be lowered to the potential production zone or the region ofinterest in the wellbore 1018 and used in conjunction with other systemcomponents such as packers, probes and pumps to perform well testing andsampling. The downhole tool 1102 may be configured to measure fluidproperties of the formation fluids, and any measurement data generatedby downhole tool 1102 and its associated operational sensors 1104 can bereal-time processed for decision-making, or communicated to a surfacelogging facility 1108 for storage, processing, and/or analysis.

FIG. 12 shows an illustrative processing system 1200 for implementingthe features and functions of the disclosed embodiments. For instance,the system 1200 may process data received from the data analysis system134 in FIG. 1, control the downhole tools 1030 and 1102 in FIGS. 10 and11 above and may implement the method 600 disclosed above.

The system 1200 may include a processor 1210, a memory 1220, a storagedevice 1230, and an input/output device 1240. Each of the components1210, 1220, 1230, and 1240 may be interconnected, for example, using asystem bus 1250. The processor 1210 may be processing instructions forexecution within the system 1200. In some embodiments, the processor1210 is a single-threaded processor, a multi-threaded processor, oranother type of processor. The processor 1210 may be capable ofprocessing instructions stored in the memory 1220 or on the storagedevice 1230. The memory 1220 and the storage device 1230 can storeinformation within the computer system 1200.

The input/output device 1240 may provide input/output operations for thesystem 1200. In some embodiments, the input/output device 1240 caninclude one or more network interface devices, e.g., an Ethernet card; aserial communication device, e.g., an RS-232 port; and/or a wirelessinterface device, e.g., an 802.11 card, a 3G wireless modem, or a 4Gwireless modem. In some embodiments, the input/output device can includedriver devices configured to receive input data and send output data toother input/output devices, e.g., keyboard, printer and display devices1260. In some embodiments, mobile computing devices, mobilecommunication devices, and other devices can be used.

In accordance with at least some embodiments, the disclosed methods andsystems related to scanning and analyzing material may be implemented indigital electronic circuitry, or in computer software, firmware, orhardware, including the structures disclosed in this specification andtheir structural equivalents, or in combinations of one or more of them.Computer software may include, for example, one or more modules ofinstructions, encoded on computer-readable storage medium for executionby, or to control the operation of, a data processing apparatus.Examples of a computer-readable storage medium include non-transitorymedium such as random-access memory (RAM) devices, read only memory(ROM) devices, optical devices (e.g., CDs or DVDs), and disk drives.

The term “data processing apparatus” encompasses all kinds of apparatus,devices, and machines for processing data, including by way of example aprogrammable processor, a computer, a system on a chip, or multipleones, or combinations, of the foregoing. The apparatus can includespecial purpose logic circuitry, e.g., an FPGA (field programmable gatearray) or an ASIC (application specific integrated circuit). Theapparatus can also include, in addition to hardware, code that createsan execution environment for the computer program in question, e.g.,code that constitutes processor firmware, a protocol stack, a databasemanagement system, an operating system, a cross-platform runtimeenvironment, a virtual machine, or a combination of one or more of them.The apparatus and execution environment can realize various differentcomputing model infrastructures, such as web services, distributedcomputing, and grid computing infrastructures.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, declarative, orprocedural languages. A computer program may, but need not, correspondto a file in a file system. A program can be stored in a portion of afile that holds other programs or data (e.g., one or more scripts storedin a markup language document), in a single file dedicated to theprogram in question, or in multiple coordinated files (e.g., files thatstore one or more modules, sub programs, or portions of code). Acomputer program may be executed on one computer or on multiplecomputers that are located at one site or distributed across multiplesites and interconnected by a communication network.

Some of the processes and logic flows described in this specificationmay be performed by one or more programmable processors executing one ormore computer programs to perform actions by operating on input data andgenerating output. The processes and logic flows may also be performedby, and apparatus may also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application specific integrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors andprocessors of any kind of digital computer. Generally, a processor willreceive instructions and data from a read-only memory or a random-accessmemory or both. A computer includes a processor for performing actionsin accordance with instructions and one or more memory devices forstoring instructions and data. A computer may also include, or beoperatively coupled to receive data from or transfer data to, or both,one or more mass storage devices for storing data, e.g., magnetic,magneto optical disks, or optical disks. However, a computer may nothave such devices. Devices suitable for storing computer programinstructions and data include all forms of non-volatile memory, mediaand memory devices, including by way of example semiconductor memorydevices (e.g., EPROM, EEPROM, flash memory devices, and others),magnetic disks (e.g., internal hard disks, removable disks, and others),magneto optical disks, and CD-ROM and DVD-ROM disks. The processor andthe memory can be supplemented by, or incorporated in, special purposelogic circuitry.

To provide for interaction with a user, operations may be implemented ona computer having a display device (e.g., a monitor, or another type ofdisplay device) for displaying information to the user and a keyboardand a pointing device (e.g., a mouse, a trackball, a tablet, a touchsensitive screen, or another type of pointing device) by which the usercan provide input to the computer. Other kinds of devices can be used toprovide for interaction with a user as well; for example, feedbackprovided to the user can be any form of sensory feedback, e.g., visualfeedback, auditory feedback, or tactile feedback; and input from theuser can be received in any form, including acoustic, speech, or tactileinput. In addition, a computer can interact with a user by sendingdocuments to and receiving documents from a device that is used by theuser; for example, by sending web pages to a web browser on a user'sclient device in response to requests received from the web browser.

A computer system may include a single computing device, or multiplecomputers that operate in proximity or generally remote from each otherand typically interact through a communication network. Examples ofcommunication networks include a local area network (“LAN”) and a widearea network (“WAN”), an inter-network (e.g., the Internet), a networkcomprising a satellite link, and peer-to-peer networks (e.g., ad hocpeer-to-peer networks). A relationship of client and server may arise byvirtue of computer programs running on the respective computers andhaving a client-server relationship to each other.

Embodiments disclosed herein include:

A. A method that includes collecting measurement data using a firstoperational sensor and a second operational sensor of a downhole tool,standardizing optical responses of each operational sensor to a mastersensor in a tool parameter space to obtain a standardized master sensorresponse, transforming the standardized master sensor response to asynthetic parameter space response of the master sensor, applying afluid model with the synthetic parameter space response of the mastersensor to predict a fluid characteristic, comparing a first predictionobtained with the fluid model from the first operational sensor with asecond prediction obtained with the fluid model from the secondoperational sensor, determining a fluid characteristic from the firstprediction and the second prediction, and optimizing a well testing andsampling operation according to the determined fluid characteristic.

B. A method that includes determining matched density predictions of afluid sample using master sensor fluid models, each of a firstoperational sensor and a second operational sensor providing arespective live density prediction, calculating a difference betweendensity predictions and measurements obtained from a densitometer, andestimating a contamination index based on the difference.

C. A system that includes a downhole tool configured to be positioned ina wellbore for oil and gas production, the downhole tool comprising afirst operational sensor and a second operational sensor each collectingmeasurement data from the wellbore, and a computer system comprising aprocessor and a memory, the computer system communicatively coupled tothe downhole tool to obtain the measurement data, and the memory storesa program that, when executed by the processor, configures the processorto standardize optical responses of each operational sensor to a mastersensor in a tool parameter space to obtain a standardized master sensorresponse, transform the standardized master sensor response to asynthetic parameter space response of the master sensor, and apply afluid model with the synthetic parameter space response of the mastersensor to predict a fluid characteristic.

Each of embodiments A, B, and C may have one or more of the followingadditional elements in any combination: Element 1: further comprisingoptimizing the standardized master sensor response obtained from thefirst and second operational sensors according to the first predictionand the second prediction. Element 2: wherein the first and secondoperational sensors are separated by a flow line and are positioned apredetermined distance from each other, and the method further comprisescollecting measurement data using the first and second operationalsensors having the same configuration and optical element design, andstandardizing the optical responses to the master sensor having the sameconfiguration and optical element design as the first and secondoperational sensors. Element 3: wherein the first and second operationalsensors are separated by a flow line and are positioned a predetermineddistance from each other, and the method further comprises, collectingmeasurement data using the first and second operational sensors havingdifferent configurations and optical element designs, and standardizingthe optical responses to the master sensor having a differentconfiguration and optical element design as the first and secondoperational sensors. Element 4: further comprising performingstandardization of the optical responses of each operational sensor tothe master sensor in tool parameter space, and by using a multi-input,multi-output neural network transformation algorithm. Element 5:calibrating the multi-input, multi-output neural network transformationalgorithm on one or more reference, and standardizing the opticalresponses of each operational sensor to the master sensor using themulti-input, multi-output neural network transformation algorithm.Element 6: calibrating the multi-input, multi-output neural networktransformation algorithm using optical responses of the first and secondoperational sensors obtained from at least one of lab testing andsimulation analysis at matched temperatures and pressures. Element 7:processing calibration data with baseline correction, normalization, andenvironmental correction, and optimizing the neural networktransformation algorithm. Element 8: transforming the standardizedmaster sensor response from the tool parameter space to the syntheticparameter space of the master sensor using a cross-space transformationalgorithm of the master sensor. Element 9: calibrating the cross-spacetransformation algorithm on the same reference fluids used forcalibrating the multi-input, multi-output cross-sensor neural networkstransformation algorithm, and calculating synthetic optical responses ofthe master sensor as a dot product of spectroscopy data of referencefluids and spectra of optical sensor elements of the first and secondoperational sensors over the same wavelength range and measured atpredetermined temperatures and pressures, followed by baselinecorrection and neutral density normalization. Element 10: applying aplurality of master sensor fluid models including a plurality ofanalyte-specific candidate models with different inputs transformed fromeach of the first and second operational sensors for each analyteprediction. Element 11: calibrating the master sensor fluid models witha nonlinear neural network or a linear partial-least-square algorithm insynthetic parameter space on a plurality of fluid samples from astandard oil library, and using synthetic master sensor responses ascandidate calibration inputs, and measured fluid compositions andproperties as calibration outputs. Element 12: wherein the fluid modelsinclude multiple candidate models for each fluid analyte or propertyprediction, and the method further comprises determining the candidatecalibration inputs using an automatic selection algorithm including abackward stepwise input selection or a forward stepwise input selection.Element 13: wherein comparing the first and second predictions comprisesobtaining the first and second predictions using same inputs for asingle phase fluid, determining a variation in the first and secondpredictions for a multi-phase fluid, estimating fluid contamination in apresence of an oil-based mud filtrate, and determining a phase of thefluid in a flow line by comparing a difference in optical responsesrecorded by the first and second operational sensors, and by measuring adensity of the fluid, calculating a bubble point of the fluid, checkinga fluid capacitance of the fluid, or checking a pumpout rate of thefluid. Element 14: wherein the measurement data collected by thedownhole tool is optimized using real-time processing or post-processingroutines, and the optimizing includes synchronizing predictions from thefirst and second operational sensors, adjusting a fluid model selection,and providing self-consistent estimation using at least one of the firstand second operational sensors.

Element 15: wherein the live density prediction is a density predictionof the fluid sample including one or more dissolved gases. Element 16:further comprising calibrating the fluid models for live densityprediction on the synthetic master sensor database without using areference fluid density measured by the densitometer as input. Element17: compensating the difference between live density prediction anddensitometer measurement for uncertainty of prediction and measurements,and a change of pumpout rate during the formation testing and sampling.Element 18: calculating the contamination index with a pre-calibratedmodel, wherein calibration data is obtained from different tools andprocessed using master sensor based on available densitometermeasurements and known lab results on the contamination index.

Element 19: wherein the processor is further configured to optimize thestandardized master sensor response obtained from the first and secondoperational sensors according to a first prediction obtained with thefluid model from the first operational sensor and a second predictionobtained with the fluid model from the second operational sensor.Element 20: wherein the fluid characteristic is determined by comparinga first prediction obtained with the fluid model from the firstoperational sensor with a second prediction obtained with the fluidmodel from the second operational sensor, and the processor is furtherconfigured to adjust a drilling parameter according to the determinedfluid characteristic.

By way of non-limiting example, exemplary combinations applicable to A:Element 4 with Element 5; Element 5 with Element 6; Element 6 withElement 7; Element 5 with Element 8; Element 8 with Element 9; Element10 with Element 11; and Element 11 with Element 12.

Therefore, the disclosed systems and methods are well adapted to attainthe ends and advantages mentioned as well as those that are inherenttherein. The particular embodiments disclosed above are illustrativeonly, as the teachings of the present disclosure may be modified andpracticed in different but equivalent manners apparent to those skilledin the art having the benefit of the teachings herein. Furthermore, nolimitations are intended to the details of construction or design hereinshown, other than as described in the claims below. It is thereforeevident that the particular illustrative embodiments disclosed above maybe altered, combined, or modified and all such variations are consideredwithin the scope of the present disclosure. The systems and methodsillustratively disclosed herein may suitably be practiced in the absenceof any element that is not specifically disclosed herein and/or anyoptional element disclosed herein. While compositions and methods aredescribed in terms of “comprising,” “containing,” or “including” variouscomponents or steps, the compositions and methods can also “consistessentially of” or “consist of” the various components and steps. Allnumbers and ranges disclosed above may vary by some amount.

Whenever a numerical range with a lower limit and an upper limit isdisclosed, any number and any included range falling within the range isspecifically disclosed. In particular, every range of values (of theform, “from about a to about b,” or, equivalently, “from approximately ato b,” or, equivalently, “from approximately a-b”) disclosed herein isto be understood to set forth every number and range encompassed withinthe broader range of values. Also, the terms in the claims have theirplain, ordinary meaning unless otherwise explicitly and clearly definedby the patentee. Moreover, the indefinite articles “a” or “an,” as usedin the claims, are defined herein to mean one or more than one of theelements that it introduces. If there is any conflict in the usages of aword or term in this specification and one or more patent or otherdocuments that may be incorporated herein by reference, the definitionsthat are consistent with this specification should be adopted.

As used herein, the phrase “at least one of” preceding a series ofitems, with the terms “and” or “or” to separate any of the items,modifies the list as a whole, rather than each member of the list (i.e.,each item). The phrase “at least one of” allows a meaning that includesat least one of any one of the items, and/or at least one of anycombination of the items, and/or at least one of each of the items. Byway of example, the phrases “at least one of A, B, and C” or “at leastone of A, B, or C” each refer to only A, only B, or only C; anycombination of A, B, and C; and/or at least one of each of A, B, and C.

What is claimed is:
 1. A method, comprising: transforming opticalresponses for a fluid sample, obtained using a first operational sensorand a second operational sensor of a downhole tool, to a parameter spaceof the downhole tool; applying fluid models in the parameter space ofthe downhole tool to the transformed optical responses to obtain densitypredictions of the fluid sample; matching the density predictions of thefirst operational sensor to the density predictions of the secondoperational sensor based on optical parameters of the fluid models toobtain matched density predictions; calculating a difference between thematched density predictions and measurements obtained from adensitometer; and estimating a contamination index based on thedifference.
 2. The method of claim 1, wherein the density predictionsare density predictions of the fluid sample including one or moredissolved gases.
 3. The method of claim 2, wherein matching the densitypredictions of the first operational sensor to the density predictionsof the second operational sensor based on the optical parameters of thefluid models comprises selecting the density predictions for the firstoperational sensor and the density predictions for the secondoperational sensor with the same optical parameters.
 4. The method ofclaim 1, further comprising transforming the optical responses from theparameter space to a synthetic parameter space using a cross-space orreverse transformation algorithm.
 5. The method of claim 4, furthercomprising calibrating the fluid models for density prediction based ona synthetic parameter space database.
 6. The method of claim 1, furthercomprising compensating for uncertainty of prediction and measurementsand a change of pumpout rate during formation testing and sampling,wherein the compensating is in the difference between the matcheddensity prediction and densitometer measurement.
 7. The method of claim1, further comprising calculating the contamination index with apre-calibrated model, wherein calibration data is obtained fromdifferent tools and processed using a parameter space based on availabledensitometer measurements and known lab results on the contaminationindex.
 8. The method of claim 1, wherein estimating the contaminationindex based on the difference comprises estimating a concentration ofoil-based mud filtrates in the fluid.
 9. A system, comprising: adownhole tool comprising a first operational sensor and a secondoperational sensor; a processor; and a computer-readable medium havinginstructions stored thereon that are executable by the processor tocause the system to, transform optical responses for a fluid sample,obtained using the first operational sensor and the second operationalsensor, to a parameter space of the downhole tool, apply fluid models inthe parameter space of the downhole tool to the transformed opticalresponses to obtain density predictions of a fluid sample, match thedensity predictions of the first operational sensor to the densitypredictions of the second operational sensor based on optical parametersof the fluid models to obtain matched density predictions, calculate adifference between the matched density predictions and measurementsobtained from a densitometer, and estimate a contamination index basedon the difference.
 10. The system of claim 9, wherein the densitypredictions are density predictions of the fluid sample including one ormore dissolved gases.
 11. The system of claim 10, wherein theinstructions to match the density predictions of the first operationalsensor to the density predictions of the second operational sensor basedon the optical parameters of the fluid models comprises instructions toselect the density predictions for the first operational sensor and thedensity predictions for the second operational sensor with the sameoptical parameters.
 12. The system of claim 9, further comprisinginstructions to transform optical responses from the first operationalsensor and the second operational sensor to a synthetic parameter spacedatabase using a cross-space or reverse transformation algorithm. 13.The system of claim 12, further comprising instructions to calibrate thefluid models for density prediction based on a synthetic parameter spacedatabase.
 14. The system of claim 9, further comprising instructions tocompensate for uncertainty of prediction and measurements and a changeof pumpout rate during formation testing and sampling, wherein thecompensating is in the difference between density prediction anddensitometer measurement.
 15. The system of claim 9, further comprisinginstructions to calculate the contamination index with a pre-calibratedmodel, wherein calibration data is obtained from different tools andprocessed using a parameter space based on available densitometermeasurements and known lab results on the contamination index.
 16. Thesystem of claim 9, wherein the instructions to estimate thecontamination index based on the difference comprise instructions toestimate a concentration of oil-based mud filtrates in the fluid.
 17. Anon-transitory, computer-readable medium having instructions storedthereon that are executable by a computing device to perform operationscomprising: transforming optical responses for a fluid sample, obtainedusing a first operational sensor and a second operational sensor of adownhole tool, to a parameter space of the downhole tool; applying fluidmodels in the parameter space of the downhole tool to the transformedoptical responses to obtain density predictions of the fluid sample;matching the density predictions of the first operational sensor to thedensity predictions of the second operational sensor based on opticalparameters of the fluid models to obtain matched density predictions;calculating a difference between the matched density predictions andmeasurements obtained from a densitometer; and estimating acontamination index based on the difference.
 18. The non-transitory,computer readable medium of claim 17, wherein the density predictionsare density predictions of the fluid sample including one or moredissolved gases.
 19. The non-transitory, computer readable medium ofclaim 17, further comprising instructions to transform optical responsesfrom the first operational sensor and the second operational sensor to asynthetic parameter space database using a cross-space or reversetransformation algorithm.
 20. The non-transitory, computer readablemedium of claim 19, further comprising instructions to calibrate thefluid models for density prediction based on a synthetic parameter spacedatabase.